DELEGATE-52: Frontier Models Corrupt ~25% of Content on Average in Long-Document Delegation
Submitted on April 17, 2026, DELEGATE-52 covers 52 professional domains and 19 models; even frontier models such as Gemini 3.1 Pro, Claude 4.6 Opus, and GPT-5.4 corrupt on average about 25% of document content by the end of long workflows.
Knowledge work agents may corrupt only a small amount of critical content during long edits, and such errors are harder to detect than outright refusals. Research shows that tool calls do not solve the problem, and degradation worsens with larger files, longer interactions, or the presence of distracting files.
The benchmark simulates long-document editing across professional domains such as code, crystallography, and musical scores, tracking integrity after each round of modification rather than only evaluating final style. Large-scale experiments reveal that sparse but severe errors accumulate with interaction, indicating that locally plausible diffs can still cause global corruption.
Document agents, code editors, and office copilots must provide structural validation, version diffs, and revertible history; one-shot generation quality cannot replace data integrity over long workflows.
High-value document automation should default to running on copies, progressively validate key fields and references, and use content corruption rate rather than saved clicks as the launch threshold.
Validation across different file formats, collaborative editing, and permission settings is needed, along with research into whether checkpoints, constrained decoding, validators, and human review can reduce cumulative corruption.